1 Setup

Con este codigo se importan todas las bases de datos y todos los paquetes necesarios.

#ANTES DE CORRER, ¡CORRER LA PESTANA "CODIGO PARA ARRANCAR TODO"!


#Set Working Directory
setwd("C:/Users/felig/Dropbox/Proyecto Juan Camilo")
rm(list=ls())

#Importar base de datos donde esta todo
library(haven)
library(readstata13)
library(tidyr)
library(plyr)
library(dplyr)
library(gridExtra)
library(ggplot2)
library(forcats)
load("C:/Users/felig/Dropbox/Proyecto Juan Camilo/MergeBases_Environment.RData")
base_stata <- read_dta("Para Stata/base_acdi_stata.dta",
                                   encoding="UTF-8")
base_stata_2019 <- read_dta("C:/Users/felig/Dropbox/Proyecto Juan Camilo/Para Stata/base_acdi_stata_2019.dta", encoding="UTF-8")

2 Donde la categoria son municipios homicidios

2.1 Separandolos por media

x2017 <- base_stata %>% select(reconciliacion_mean, disculpas_mean, violencia_mean, confianza_vec_mean,confianza_medios_mean, confianza_instituciones_mean)
x2019 <- base_stata_2019 %>% select(ends_with("mean2019"), ends_with("men2019"))

par(mar=c(4,2,0.1,0.1))
for(i in 1:length(x2017)){
   
  plot <- 
    
    base_stata %>%
    filter(dummyPAR==1) %>% 
  mutate(mun_homicidio=(ifelse(homi_cienmil_mean>mean(homi_cienmil_mean),
                             "Alta homicidios","Baja homicidios"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_homicidio)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

par(mar=c(4,2,0.1,0.1))

for(i in 1:length(x2019)){
   
  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_homicidio=(ifelse(homi_cienmil_mean>mean(homi_cienmil_mean),
                             "Alta homicidios","Baja homicidios"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_homicidio)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

2.2 Separandolos en Q1 y Q3

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
        filter(dummyPAR==1) %>% 
  mutate(mun_homicidio=as.factor((ifelse
                       (homi_cienmil_mean>quantile(homi_cienmil_mean, 0.75),
                         "Alta homicidios",
                         ifelse(homi_cienmil_mean<quantile(homi_cienmil_mean, 0.25),
                         "Baja homicidios",
                         NA))))) %>% 
    filter(mun_homicidio!=is.na(mun_homicidio)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_homicidio)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

 par(mfrow=c(2,4))


for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
    
  mutate(mun_homicidio=as.factor((ifelse
                       (homi_cienmil_mean>quantile(homi_cienmil_mean, 0.75),
                         "Alta homicidios",
                         ifelse(homi_cienmil_mean<quantile(homi_cienmil_mean, 0.25),
                         "Baja homicidios",
                         NA))))) %>% 
    filter(mun_homicidio!=is.na(mun_homicidio)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_homicidio)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

2.3 Separandolos entre el maximo y el minimo

 par(mfrow=c(2,4))

for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
        filter(dummyPAR==1) %>% 
  mutate(mun_homicidio=as.factor((ifelse
                       (homi_cienmil_mean==max(homi_cienmil_mean),
                         "Alta homicidios",
                         ifelse(homi_cienmil_mean==min(homi_cienmil_mean),
                         "Baja homicidios",
                         NA))))) %>% 
    filter(mun_homicidio!=is.na(mun_homicidio)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_homicidio)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_homicidio=as.factor((ifelse
                       (homi_cienmil_mean==max(homi_cienmil_mean),
                         "Alta homicidios",
                         ifelse(homi_cienmil_mean==min(homi_cienmil_mean),
                         "Baja homicidios",
                         NA))))) %>% 
    filter(mun_homicidio!=is.na(mun_homicidio)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_homicidio)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

3 Donde la categoria son municipios violentados

3.1 Separandolos por media

par(mar=c(4,2,0.1,0.1))
for(i in 1:length(x2017)){
   
  plot <- 
    
    base_stata %>% 
        filter(dummyPAR==1) %>% 
  mutate(mun_violentado=(ifelse(Ataques_Pobl_Civil_mean>mean(Ataques_Pobl_Civil_mean),
                             "violentado","No violentado"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

x <- base_stata_2019 %>% select(ends_with("mean2019"), ends_with("men2019"))
for(i in 1:length(x2019)){
   
  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_violentado=(ifelse(Ataques_Pobl_Civil_mean>mean(Ataques_Pobl_Civil_mean),
                             "violentado","No violentado"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

3.2 Separandolos en Q1 y Q3

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (Ataques_Pobl_Civil_mean>quantile(Ataques_Pobl_Civil_mean, 0.75),
                         "violentado",
                         ifelse(Ataques_Pobl_Civil_mean<quantile(Ataques_Pobl_Civil_mean, 0.25),
                         "No violentado",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (Ataques_Pobl_Civil_mean>quantile(Ataques_Pobl_Civil_mean, 0.75),
                         "violentado",
                         ifelse(Ataques_Pobl_Civil_mean<quantile(Ataques_Pobl_Civil_mean, 0.25),
                         "No violentado",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

3.3 Separandolos entre el maximo y el minimo

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (Ataques_Pobl_Civil_mean==max(Ataques_Pobl_Civil_mean),
                         "violentado",
                         ifelse(Ataques_Pobl_Civil_mean==min(Ataques_Pobl_Civil_mean),
                         "No violentado",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (Ataques_Pobl_Civil_mean==max(Ataques_Pobl_Civil_mean),
                         "violentado",
                         ifelse(Ataques_Pobl_Civil_mean==min(Ataques_Pobl_Civil_mean),
                         "No violentado",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

4 Donde la categoria son municipios con presencia de victimas (secuestro)

4.1 Separandolos por media

par(mar=c(4,2,0.1,0.1))
for(i in 1:length(x2017)){
   
  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_secuestrado=(ifelse(secuestro_cienmil_mean>mean(secuestro_cienmil_mean),
                             "Alto secuestro","Bajo secuestro"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_secuestrado)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

for(i in 1:length(x2019)){
   
  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_secuestrado=(ifelse(secuestro_cienmil_mean>mean(secuestro_cienmil_mean),
                             "Alto secuestro","Bajo secuestro"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_secuestrado)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

4.2 Separandolos en Q1 y Q3

 par(mfrow=c(2,4))


for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_secuestrado=as.factor((ifelse
                       (secuestro_cienmil_mean>quantile(secuestro_cienmil_mean, 0.75),
                         "Alto secuestro",
                         ifelse(secuestro_cienmil_mean<quantile(secuestro_cienmil_mean, 0.25),
                         "Bajo secuestro",
                         NA))))) %>% 
    filter(mun_secuestrado!=is.na(mun_secuestrado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_secuestrado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

 par(mfrow=c(2,4))


for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_secuestrado=as.factor((ifelse
                       (secuestro_cienmil_mean>quantile(secuestro_cienmil_mean, 0.75),
                         "Alto secuestro",
                         ifelse(secuestro_cienmil_mean<quantile(secuestro_cienmil_mean, 0.25),
                         "Bajo secuestro",
                         NA))))) %>% 
    filter(mun_secuestrado!=is.na(mun_secuestrado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_secuestrado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

4.3 Separandolos entre el maximo y el minimo

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (secuestro_cienmil_mean==max(secuestro_cienmil_mean),
                         "Alto secuestro",
                         ifelse(secuestro_cienmil_mean==min(secuestro_cienmil_mean),
                         "Bajo secuestro",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (secuestro_cienmil_mean==max(secuestro_cienmil_mean),
                         "Alto secuestro",
                         ifelse(secuestro_cienmil_mean==min(secuestro_cienmil_mean),
                         "Bajo secuestro",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019",y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

5 Donde la categoria son municipios con presencia de desmoviliazdos

5.1 Separandolos por media

par(mar=c(4,2,0.1,0.1))
for(i in 1:length(x2017)){
   
  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_desmovilizado=(ifelse(desmovilizados_cienmil_mean>mean(desmovilizados_cienmil_mean),
                             "Alto desmovilizado","Bajo desmovilizado"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_desmovilizado)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

for(i in 1:length(x2019)){
   
  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_desmovilizado=(ifelse(desmovilizados_cienmil_mean>mean(desmovilizados_cienmil_mean),
                             "Alto desmovilizado","Bajo desmovilizado"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_desmovilizado)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

5.2 Separandolos en Q1 y Q3

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_desmovilizado=as.factor((ifelse
                       (desmovilizados_cienmil_mean>quantile(desmovilizados_cienmil_mean, 0.75),
                         "Alto desmovilizado",
                         ifelse(desmovilizados_cienmil_mean<quantile(desmovilizados_cienmil_mean, 0.25),
                         "Bajo desmovilizado",
                         NA))))) %>% 
    filter(mun_desmovilizado!=is.na(mun_desmovilizado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_desmovilizado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_desmovilizado=as.factor((ifelse
                       (desmovilizados_cienmil_mean>quantile(desmovilizados_cienmil_mean, 0.75),
                         "Alto desmovilizado",
                         ifelse(desmovilizados_cienmil_mean<quantile(desmovilizados_cienmil_mean, 0.25),
                         "Bajo desmovilizado",
                         NA))))) %>% 
    filter(mun_desmovilizado!=is.na(mun_desmovilizado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_desmovilizado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019",y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

5.3 Separandolos entre el maximo y el minimo

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_violentado=as.factor((ifelse
                       (desmovilizados_cienmil_mean==max(desmovilizados_cienmil_mean),
                         "Alto desmovilizado",
                         ifelse(desmovilizados_cienmil_mean==min(desmovilizados_cienmil_mean),
                         "Bajo desmovilizado",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 

  mutate(mun_violentado=as.factor((ifelse
                       (desmovilizados_cienmil_mean==max(desmovilizados_cienmil_mean),
                         "Alto desmovilizado",
                         ifelse(desmovilizados_cienmil_mean==min(desmovilizados_cienmil_mean),
                         "Bajo desmovilizado",
                         NA))))) %>% 
    filter(mun_violentado!=is.na(mun_violentado)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_violentado)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019",y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

6 Donde la categoria son municipios pobres

6.1 Separandolos por media

par(mar=c(4,2,0.1,0.1))
for(i in 1:length(x2017)){
   
  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_pobre=(ifelse(nbi_mean>mean(nbi_mean),
                             "Alta pobreza","Baja pobreza"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_pobre)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

for(i in 1:length(x2019)){
   
  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_pobre=(ifelse(nbi_mean>mean(nbi_mean),
                             "Alta pobreza","Baja pobreza"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_pobre)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

6.2 Separandolos en Q1 y Q3

 par(mfrow=c(2,4))

for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_pobre=as.factor((ifelse
                       (nbi_mean>quantile(nbi_mean, 0.75),
                         "Alta pobreza",
                         ifelse(nbi_mean<quantile(nbi_mean, 0.25),
                         "Baja pobreza",
                         NA))))) %>% 
    filter(mun_pobre!=is.na(mun_pobre)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_pobre)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_pobre=as.factor((ifelse
                       (nbi_mean>quantile(nbi_mean, 0.75),
                         "Alta pobreza",
                         ifelse(nbi_mean<quantile(nbi_mean, 0.25),
                         "Baja pobreza",
                         NA))))) %>% 
    filter(mun_pobre!=is.na(mun_pobre)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_pobre)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019",y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

6.3 Separandolos entre el maximo y el minimo

 par(mfrow=c(2,4))
x <- base_stata[,165:207] %>% select(ends_with("mean"))


for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_pobre=as.factor((ifelse
                       (nbi_mean==max(nbi_mean),
                         "Alta pobreza",
                         ifelse(nbi_mean==min(nbi_mean),
                         "Baja pobreza",
                         NA))))) %>% 
    filter(mun_pobre!=is.na(mun_pobre)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_pobre)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_pobre=as.factor((ifelse
                       (nbi_mean==max(nbi_mean),
                         "Alta pobreza",
                         ifelse(nbi_mean==min(nbi_mean),
                         "Baja pobreza",
                         NA))))) %>% 
    filter(mun_pobre!=is.na(mun_pobre)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_pobre)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019",y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

7 Donde la categoria son municipios con presencia de establecimientos educativos

7.1 Separandolos por media

par(mar=c(4,2,0.1,0.1))
for(i in 1:length(x2017)){
   
  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_establ=(ifelse(t_establ_mean>mean(t_establ_mean),
                             "Alta presencia Establecimientos Educativos","Baja presencia Establecimientos Educativos"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_establ)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

for(i in 1:length(x2019)){
   
  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_establ=(ifelse(t_establ_mean>mean(t_establ_mean),
                             "Alta presencia Establecimientos Educativos","Baja presencia Establecimientos Educativos"))) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_establ)) +
  geom_bar(stat="identity", fill = "white")+
  theme_minimal()+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  print(plot)
}

7.2 Separandolos en Q1 y Q3

 par(mfrow=c(2,4))



for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_establ=as.factor((ifelse
                       (t_establ_mean>quantile(t_establ_mean, 0.75),
                         "Alta presencia Establecimientos Educativos",
                         ifelse(t_establ_mean<quantile(t_establ_mean, 0.25),
                         "Baja presencia Establecimientos Educativos",
                         NA))))) %>% 
    filter(mun_establ!=is.na(mun_establ)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_establ)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017", y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_establ=as.factor((ifelse
                       (t_establ_mean>quantile(t_establ_mean, 0.75),
                         "Alta presencia Establecimientos Educativos",
                         ifelse(t_establ_mean<quantile(t_establ_mean, 0.25),
                         "Baja presencia Establecimientos Educativos",
                         NA))))) %>% 
    filter(mun_establ!=is.na(mun_establ)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_establ)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019", y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
}

7.3 Separandolos entre el maximo y el minimo

par(mfrow=c(2,4))
x <- base_stata[,165:207] %>% select(ends_with("mean"))


for(i in 1:length(x2017)){

  plot <- 
    
    base_stata %>% 
    filter(dummyPAR==1) %>% 
  mutate(mun_establ=as.factor((ifelse
                       (t_establ_mean==max(t_establ_mean),
                         "Alta presencia Establecimientos Educativos",
                         ifelse(t_establ_mean==min(t_establ_mean),
                         "Baja presencia Establecimientos Educativos",
                         NA))))) %>% 
    filter(mun_establ!=is.na(mun_establ)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2017)[i]]], col=mun_establ)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2017",y=names(x2017[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
} 

for(i in 1:length(x2019)){

  plot <- 
    
    base_stata_2019 %>% 
  mutate(mun_establ=as.factor((ifelse
                       (t_establ_mean==max(t_establ_mean),
                         "Alta presencia Establecimientos Educativos",
                         ifelse(t_establ_mean==min(t_establ_mean),
                         "Baja presencia Establecimientos Educativos",
                         NA))))) %>% 
    filter(mun_establ!=is.na(mun_establ)) %>% 
  ggplot(aes(x=Municipio, y=.[[names(x2019)[i]]], col=mun_establ)) +
  geom_bar(stat="identity", na.rm=TRUE, fill="white")+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  labs(title="Mpios PAR para el 2019",y=names(x2019[i]))+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
  
  print(plot)
} 

8 Pruebas T

base_stata_2017 <-  base_stata %>% filter(dummyPAR==1)

for (i in 1:length(x2017)){
  
  ttest <- t.test(as.data.frame(base_stata_2017[names(x2017)][i]),as.data.frame(base_stata_2019[names(x2019)][i]))
  
  print(ttest)
}
## 
##  Welch Two Sample t-test
## 
## data:  as.data.frame(base_stata_2017[names(x2017)][i]) and as.data.frame(base_stata_2019[names(x2019)][i])
## t = 6.794, df = 40.548, p-value = 3.401e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  2.155696 3.980260
## sample estimates:
## mean of x mean of y 
## 3.3107970 0.2428193 
## 
## 
##  Welch Two Sample t-test
## 
## data:  as.data.frame(base_stata_2017[names(x2017)][i]) and as.data.frame(base_stata_2019[names(x2019)][i])
## t = 14.187, df = 51.939, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.8496715 1.1296346
## sample estimates:
##  mean of x  mean of y 
##  0.8295794 -0.1600736 
## 
## 
##  Welch Two Sample t-test
## 
## data:  as.data.frame(base_stata_2017[names(x2017)][i]) and as.data.frame(base_stata_2019[names(x2019)][i])
## t = -1.3726, df = 51.917, p-value = 0.1758
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4086848  0.0766831
## sample estimates:
## mean of x mean of y 
## -1.256594 -1.090593 
## 
## 
##  Welch Two Sample t-test
## 
## data:  as.data.frame(base_stata_2017[names(x2017)][i]) and as.data.frame(base_stata_2019[names(x2019)][i])
## t = 5.2054, df = 37.421, p-value = 7.255e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.2185224 0.4968931
## sample estimates:
## mean of x mean of y 
##  2.505096  2.147388 
## 
## 
##  Welch Two Sample t-test
## 
## data:  as.data.frame(base_stata_2017[names(x2017)][i]) and as.data.frame(base_stata_2019[names(x2019)][i])
## t = 7.8051, df = 34.305, p-value = 4.134e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  3.572252 6.086258
## sample estimates:
## mean of x mean of y 
##  18.36326  13.53401 
## 
## 
##  Welch Two Sample t-test
## 
## data:  as.data.frame(base_stata_2017[names(x2017)][i]) and as.data.frame(base_stata_2019[names(x2019)][i])
## t = 12.234, df = 33.506, p-value = 6.573e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  11.76371 16.45352
## sample estimates:
## mean of x mean of y 
##  39.50485  25.39623

9 Graficos generales

#Creacion de la bsae de datos para poder comparar los graficos por ano. 
ano2017 <- c()
ano2017[1:27] <- 2017
ano2017 <- t(ano2017)
 
ano2019 <- c()
ano2019[1:27] <- 2019
ano2019 <- t(ano2019)


#Aqui juntamos los valores de interes de la ola 2017 y 2019. 
base_stata_2019_tidy <- cbind(base_stata_2019[1],base_stata_2017[names(x2017)], base_stata_2019[names(x2019)])
                              
                              
#Aqui se hace el tidy para poder generar la variable "indicador"
base_stata_2019_tidy_ensayo <- base_stata_2019_tidy %>%   gather(key=indicador, value=valor, -Municipio)
## Warning: attributes are not identical across measure variables;
## they will be dropped
#Creamos la columna para distinguir las olas
base_stata_2019_tidy_ensayo$ano <- 0
base_stata_2019_tidy_ensayo[1:162,4] <- 2017
base_stata_2019_tidy_ensayo[163:324,4] <- 2019


#Limpiamos nombres para que todos encajen en uno mismo. 
base_stata_2019_tidy_ensayo$indicador <-  gsub("_mean","", base_stata_2019_tidy_ensayo$indicador, ignore.case = TRUE)
base_stata_2019_tidy_ensayo$indicador <-  gsub("2019","", base_stata_2019_tidy_ensayo$indicador, ignore.case = TRUE)
base_stata_2019_tidy_ensayo$indicador <-  gsub("_men","", base_stata_2019_tidy_ensayo$indicador, ignore.case = TRUE)


base_stata_2019_tidy_ensayo %>%
ggplot(aes(x=as.factor(indicador), y=valor, col=as.factor(ano))) +
geom_point()+
facet_wrap(~Municipio)+
labs(title="Comparacion Mpios PAR 2017 y 2019")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))

base_stata_2019_tidy_ensayo %>%
ggplot(aes(x=Municipio, y=valor, col=as.factor(ano))) +
geom_point()+
facet_wrap(~as.factor(indicador))+
labs(title="Comparacion Mpios PAR 2017 y 2019")+
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))

10 Graficos por municipio y por indicador (se hace zoom)

#Volver categoricas las variables categoricas. 
base_stata_2019_tidy_ensayo$indicador <-  as.factor(base_stata_2019_tidy_ensayo$indicador)



  
  for (i in 1:length(unique(base_stata_2019_tidy_ensayo$Municipio))){
    
    plot <- base_stata_2019_tidy_ensayo %>%
      ggplot(aes(x=as.factor(ano), y=valor, col=Municipio[i])) +
      geom_point()+
      facet_wrap(~indicador)+
      labs(title="Comparacion Mpios PAR 2017 y 2019")+
      theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle=90))
    
    print(plot)
    
}

11 Exploraciones para ver variaciones chéveres